{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Test](https://github.com/tmbdev/webdataset/workflows/Test/badge.svg)](https://github.com/tmbdev/webdataset/actions?query=workflow%3ATest)\n",
    "[![DeepSource](https://static.deepsource.io/deepsource-badge-light-mini.svg)](https://deepsource.io/gh/tmbdev/webdataset/?ref=repository-badge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.utils.data\n",
    "import torch.nn\n",
    "from random import randrange\n",
    "import os\n",
    "os.environ[\"WDS_VERBOSE_CACHE\"] = \"1\"\n",
    "os.environ[\"GOPEN_VERBOSE\"] = \"0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The WebDataset Format\n",
    "\n",
    "WebDataset format files are tar files, with two conventions:\n",
    "\n",
    "- within each tar file, files that belong together and make up a training sample share the same basename when stripped of all filename extensions\n",
    "- the shards of a tar file are numbered like `something-000000.tar` to `something-012345.tar`, usually specified using brace notation `something-{000000..012345}.tar`\n",
    "\n",
    "WebDataset can read files from local disk or from any pipe, which allows it to access files using common cloud object stores. WebDataset can also read concatenated MsgPack and CBORs sources.\n",
    "\n",
    "The WebDataset representation allows writing purely sequential I/O pipelines for large scale deep learning. This is important for achieving high I/O rates from local storage (3x-10x for local drives compared to random access) and for using object stores and cloud storage for training.\n",
    "\n",
    "The WebDataset format represents images, movies, audio, etc. in their native file formats, making the creation of WebDataset format data as easy as just creating a tar archive. Because of the way data is aligned, WebDataset works well with block deduplication as well and aligns data on predictable boundaries.\n",
    "\n",
    "Standard tools can be used for accessing and processing WebDataset-format files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PMC4991227_00003.json\n",
      "PMC4991227_00003.png\n",
      "PMC4537884_00002.json\n",
      "PMC4537884_00002.png\n",
      "PMC4323233_00003.json\n",
      "PMC4323233_00003.png\n",
      "PMC5429906_00004.json\n",
      "PMC5429906_00004.png\n",
      "PMC5592712_00002.json\n",
      "PMC5592712_00002.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tar: stdout: write error\n"
     ]
    }
   ],
   "source": [
    "bucket = \"https://storage.googleapis.com/webdataset/testdata/\"\n",
    "dataset = \"publaynet-train-{000000..000009}.tar\"\n",
    "\n",
    "url = bucket + dataset\n",
    "!curl -s {url} | tar tf - | sed 10q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in these `.tar` files, we have pairs of `.json` and `.png` files; each such pair makes up a training sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WebDataset Libraries\n",
    "\n",
    "There are several libraries supporting the WebDataset format:\n",
    "\n",
    "- `webdataset` for Python3 (includes the `wids` library), this repository\n",
    "- [Webdataset.jl](https://github.com/webdataset/WebDataset.jl) a Julia implementation\n",
    "- [tarp](https://github.com/webdataset/tarp), a Golang implementation and command line tool\n",
    "- Ray Data sources and sinks\n",
    "\n",
    "The `webdataset` library can be used with PyTorch, Tensorflow, and Jax."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The `webdataset` Library\n",
    "\n",
    "The `webdataset` library is an implementation of PyTorch `IterableDataset` (or a mock implementation thereof if you aren't using PyTorch). It implements as form of stream processing. Some of its features are:\n",
    "\n",
    "- large scale parallel data access through sharding\n",
    "- high performance disk I/O due to purely sequential reads\n",
    "- latency insensitive due to big fat pipes\n",
    "- no local storage required\n",
    "- instant startup for training jobs\n",
    "- only requires reading from file descriptors/network streams, no special APIs\n",
    "- its API encourages high performance I/O pipelines\n",
    "- scalable from tiny desktop datasets to petascale datasets\n",
    "- provides local caching if desired\n",
    "- requires no dataset metadata; any collection of shards can be read and used instantly\n",
    "\n",
    "The main limitations people run into are related to the fact that `IterableDataset` is less commonly used in PyTorch and some existing code may not support it as well, and that achieving an exactly balanced number of training samples across many compute nodes for a fixed epoch size is tricky; for multinode training, `webdataset` is usually used with shard resampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two interfaces, the concise \"fluid\" interface and a longer \"pipeline\" interface. We'll show examples using the fluid interface, which is usually what you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import webdataset as wds\n",
    "pil_dataset = wds.WebDataset(url).shuffle(1000).decode(\"pil\").to_tuple(\"png\", \"json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting datasets are standard PyTorch `IterableDataset` instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(pil_dataset, torch.utils.data.IterableDataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6e34732dd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for image, json in pil_dataset:\n",
    "    break\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add onto the existing pipeline for augmentation and data preparation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6e347a1c90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torchvision.transforms as transforms\n",
    "from PIL import Image\n",
    "\n",
    "preproc = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    lambda x: 1-x,\n",
    "])\n",
    "\n",
    "def preprocess(sample):\n",
    "    image, json = sample\n",
    "    try:\n",
    "        label = json[\"annotations\"][0][\"category_id\"]\n",
    "    except:\n",
    "        label = 0\n",
    "    return preproc(image), label\n",
    "\n",
    "dataset = pil_dataset.map(preprocess)\n",
    "\n",
    "for image, label in dataset:\n",
    "    break\n",
    "plt.imshow(image.numpy().transpose(1, 2, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`WebDataset` is just an instance of a standard `IterableDataset`. It's a single-threaded way of iterating over a dataset. Since image decompression and data augmentation can be compute intensive, PyTorch usually uses the `DataLoader` class to parallelize data loading and preprocessing. `WebDataset` is fully compatible with the standard `DataLoader`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `wds.WebDataset` fluid interface is just a convenient shorthand for writing down pipelines. The underlying pipeline is an instance of the `wds.DataPipeline` class, and you can construct data pipelines explicitly, similar to the way you use `nn.Sequential` inside models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([16, 3, 224, 224]), (16,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = wds.DataPipeline(\n",
    "    wds.SimpleShardList(url),\n",
    "\n",
    "    # at this point we have an iterator over all the shards\n",
    "    wds.shuffle(100),\n",
    "\n",
    "    # add wds.split_by_node here if you are using multiple nodes\n",
    "    wds.split_by_worker,\n",
    "\n",
    "    # at this point, we have an iterator over the shards assigned to each worker\n",
    "    wds.tarfile_to_samples(),\n",
    "\n",
    "    # this shuffles the samples in memory\n",
    "    wds.shuffle(1000),\n",
    "\n",
    "    # this decodes the images and json\n",
    "    wds.decode(\"pil\"),\n",
    "    wds.to_tuple(\"png\", \"json\"),\n",
    "    wds.map(preprocess),\n",
    "    wds.shuffle(1000),\n",
    "    wds.batched(16)\n",
    ")\n",
    "\n",
    "batch = next(iter(dataset))\n",
    "batch[0].shape, batch[1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a number of notebooks showing how to use WebDataset for image classification and LLM training:\n",
    "\n",
    "- [train-resnet50-wds](examples/train-resnet50-wds.ipynb) -- simple, single GPU training from Imagenet\n",
    "- [train-resnet50-multiray-wds](examples/train-resnet50-multiray-wds.ipynb) -- multinode training using webdataset\n",
    "- [generate-text-dataset](examples/generate-text-dataset.ipynb) -- initial dataset generation\n",
    "- [tesseract-wds](examples/tesseract-wds.ipynb) -- shard-to-shard transformations, here for OCR running over large dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installation and Documentation\n",
    "\n",
    "    $ pip install webdataset\n",
    "\n",
    "For the Github version:\n",
    "\n",
    "    $ pip install git+https://github.com/tmbdev/webdataset.git\n",
    "\n",
    "Here are some videos talking about WebDataset and large scale deep learning:\n",
    "\n",
    "- [Introduction to Large Scale Deep Learning](https://www.youtube.com/watch?v=kNuA2wflygM)\n",
    "- [Loading Training Data with WebDataset](https://www.youtube.com/watch?v=mTv_ePYeBhs)\n",
    "- [Creating Datasets in WebDataset Format](https://www.youtube.com/watch?v=v_PacO-3OGQ)\n",
    "- [Tools for Working with Large Datasets](https://www.youtube.com/watch?v=kIv8zDpRUec)\n",
    "\n",
    "Examples: (NB: some of these are for older versions of WebDataset, but the differences should be small)\n",
    "\n",
    "- [loading videos](https://github.com/tmbdev/webdataset/blob/master/docs/video-loading-example.ipynb)\n",
    "- [splitting raw videos into clips for training](https://github.com/tmbdev/webdataset/blob/master/docs/ytsamples-split.ipynb)\n",
    "- [converting the Falling Things dataset](https://github.com/tmbdev/webdataset/blob/master/docs/falling-things-make-shards.ipynb)\n",
    "\n",
    "# Dependencies\n",
    "\n",
    "The WebDataset library only requires PyTorch, NumPy, and a small library called `braceexpand`.\n",
    "\n",
    "WebDataset loads a few additional libraries dynamically only when they are actually needed and only in the decoder:\n",
    "\n",
    "- PIL/Pillow for image decoding\n",
    "- `torchvision`, `torchvideo`, `torchaudio` for image/video/audio decoding\n",
    "- `msgpack` for MessagePack decoding\n",
    "- the `curl` command line tool for accessing HTTP servers\n",
    "- the Google/Amazon/Azure command line tools for accessing cloud storage buckets\n",
    "\n",
    "Loading of one of these libraries is triggered by configuring a decoder that attempts to decode content in the given format and encountering a file in that format during decoding. (Eventually, the torch... dependencies will be refactored into those libraries.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Decoding\n",
    "\n",
    "Data decoding is a special kind of transformations of samples. You could simply write a decoding function like this:\n",
    "\n",
    "```Python\n",
    "def my_sample_decoder(sample):\n",
    "    result = dict(__key__=sample[\"__key__\"])\n",
    "    for key, value in sample.items():\n",
    "        if key == \"png\" or key.endswith(\".png\"):\n",
    "            result[key] = mageio.imread(io.BytesIO(value))\n",
    "        elif ...:\n",
    "            ...\n",
    "    return result\n",
    "\n",
    "dataset = wds.Processor(wds.map, my_sample_decoder)(dataset)\n",
    "```\n",
    "\n",
    "This gets tedious, though, and it also unnecessarily hardcodes the sample's keys into the processing pipeline. To help with this, there is a helper class that simplifies this kind of code. The primary use of `Decoder` is for decoding compressed image, video, and audio formats, as well as unzipping `.gz` files.\n",
    "\n",
    "Here is an example of automatically decoding `.png` images with `imread` and using the default `torch_video` and `torch_audio` decoders for video and audio:\n",
    "\n",
    "```Python\n",
    "def my_png_decoder(key, value):\n",
    "    if not key.endswith(\".png\"):\n",
    "        return None\n",
    "    assert isinstance(value, bytes)\n",
    "    return imageio.imread(io.BytesIO(value))\n",
    "\n",
    "dataset = wds.Decoder(my_png_decoder, wds.torch_video, wds.torch_audio)(dataset)\n",
    "```\n",
    "\n",
    "You can use whatever criteria you like for deciding how to decode values in samples. When used with standard `WebDataset` format files, the keys are the full extensions of the file names inside a `.tar` file. For consistency, it's recommended that you primarily rely on the extensions (e.g., `.png`, `.mp4`) to decide which decoders to use. There is a special helper function that simplifies this:\n",
    "\n",
    "```Python\n",
    "def my_decoder(value):\n",
    "    return imageio.imread(io.BytesIO(value))\n",
    "    \n",
    "dataset = wds.Decoder(wds.handle_extension(\".png\", my_decoder))(dataset)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Smaller\" Datasets and Desktop Computing\n",
    "\n",
    "WebDataset is an ideal solution for training on petascale datasets kept on high performance distributed data stores like AIStore, AWS/S3, and Google Cloud. Compared to data center GPU servers, desktop machines have much slower network connections, but training jobs on desktop machines often also use much smaller datasets. WebDataset also is very useful for such smaller datasets, and it can easily be used for developing and testing on small datasets and then scaling up to large datasets by simply using more shards.\n",
    "\n",
    "\n",
    "Here are different usage scenarios:\n",
    "\n",
    "- **desktop deep learning, smaller datasets**\n",
    "    - copy all shards to local disk manually\n",
    "    - use automatic shard caching\n",
    "- **prototyping, development, testing of jobs for large scale training**\n",
    "    - copy a small subset of shards to local disk\n",
    "    - use automatic shard caching with a small subrange of shards\n",
    "- **cloud training against cloud buckets**\n",
    "    - use WebDataset directly with remote URLs\n",
    "- **on premises training with high performance store (e.g., AIStore) and fast networks**\n",
    "    - use WebDataset directly with remote URLs\n",
    "- **on premises training with slower object stores and/or slower networks**\n",
    "    - use automatic shard caching\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Location Independence, Caching, Etc.\n",
    "\n",
    "WebDataset makes it easy to use a single specification for your datasets and run your code without change in different environments.\n",
    "\n",
    "## Loadable Dataset Specifications\n",
    "\n",
    "If you write your input pipelines such that they are defined by a dataset specification in some language, you can most easily retarget your training pipelines to different datasets. You can do this either by dynamically loading the Python code that constructs the pipeline or by using a YAML/JSON dataset specification. \n",
    "\n",
    "A YAML dataset specification looks like this:\n",
    "\n",
    "```\n",
    "dataset:\n",
    "  - shards: gs://nvdata-ocropus-tess/ia1-{000000..000033}.tar\n",
    "    scaleprob: 0.3\n",
    "  - shards: gs://nvdata-ocropus-tess/cdipsub-{000000..000022}.tar\n",
    "    scale: [1.0, 3.0]\n",
    "  - shards: gs://nvdata-ocropus-tess/gsub-{000000..000167}.tar\n",
    "    scale: [0.4, 1.0]\n",
    "  - shards: gs://nvdata-ocropus-tess/bin-gsub-{000000..000167}.tar\n",
    "    extensions: nrm.jpg\n",
    "    scale: [0.3, 1.0]\n",
    "  - shards: gs://nvdata-ocropus/rendered.tar\n",
    "    scaleprob: 1.0\n",
    "```\n",
    "\n",
    "Note that datasets can be composed from different shard collections, mixed in different proportions.\n",
    "\n",
    "The dataset specification reader will be integrated in the next minor version update.\n",
    "\n",
    "## AIStore Proxy\n",
    "\n",
    "\n",
    "If you want to use an AISTore server as a cache, you can tell any WebDataset pipeline to replace direct accesses to your URLs to proxied accesses via the AIStore server. To do that, you need to set a couple of environment variables.\n",
    "\n",
    "```\n",
    "export AIS_ENDPOINT=http://nix:51080\n",
    "export USE_AIS_FOR=\"gs\"\n",
    "```\n",
    "\n",
    "Now, any accesses to Google Cloud Storage (`gs://` urls) will be routed through the AIS server.\n",
    "\n",
    "## URL Rewriting\n",
    "\n",
    "You can rewrite URLs using regular expressions via an environment variable; the syntax is `WDS_REWRITE=regex=regex;regex=regex`.\n",
    "\n",
    "For example, to replace `gs://` accesses with local file accesses, use\n",
    "\n",
    "```\n",
    "export WDS_REWRITE=\"gs://=/shared/data/\"\n",
    "```\n",
    "\n",
    "To access Google cloud data via ssh, you might use something like:\n",
    "\n",
    "```\n",
    "export WDS_REWRITE=\"gs://=pipe:ssh proxyhost gsutil cat \"\n",
    "```\n",
    "\n",
    "## Use the Caching Mechanism\n",
    "\n",
    "If you use the built-in caching mechanism, you can simply download shards to a local directory and specify that directory as the cache directory. The shards in that directory will override the shards that are being downloaded. Shards in the cache are mapped based on the pathname and file name of your shard names.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct Copying of Shards\n",
    "\n",
    "Let's take the OpenImages dataset as an example; it's half a terabyte large. For development and testing, you may not want to download the entire dataset, but you may also not want to use the dataset remotely. With WebDataset, you can just download a small number of shards and use them during development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl -L -s http://storage.googleapis.com/nvdata-openimages/openimages-train-000000.tar > /tmp/openimages-train-000000.tar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"{'__key__': 'e39871fd9fd74f55', '__url__': '/tmp/openimages-train-000000.tar', 'jpg': b'\\\\xff\\\\xd8\\\\xff\\\\xe0\\\\x00\\\\x10JFIF\\\\x00\\\\x01\\\\x01\\\\x01\\\\x01:\\\\x01:\\\\x00\\\\x00\\\\xff\\\\xdb\\\\x00C\\\\x00\\\\x06\\\\x04\\\\x05\\\\x06\\\\x05\\\\x04\\\\x06\\\\x06\\\\\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = wds.DataPipeline(\n",
    "    wds.SimpleShardList(\"/tmp/openimages-train-000000.tar\"),\n",
    "    wds.tarfile_to_samples(),\n",
    ")\n",
    "repr(next(iter(dataset)))[:200]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the WebDataset class works the same way on local files as it does on remote files. Furthermore, unlike other kinds of dataset formats and archive formats, downloaded datasets are immediately useful and don't need to be unpacked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a WebDataset\n",
    "\n",
    "## Using `tar`\n",
    "\n",
    "Since WebDatasets are just regular tar files, you can usually create them by just using the `tar` command. All you have to do is to arrange for any files that should be in the same sample to share the same basename. Many datasets already come that way. For those, you can simply create a WebDataset with\n",
    "\n",
    "```\n",
    "$ tar --sort=name -cf dataset.tar dataset/\n",
    "```\n",
    "\n",
    "If your dataset has some other directory layout, you may need a different file name in the archive from the name on disk. You can use the `--transform` argument to GNU tar to transform file names. You can also use the `-T` argument to read the files from a text file and embed other options in that text file.\n",
    "\n",
    "## The `tarp create` Command\n",
    "\n",
    "The [`tarp`](https://github.com/tmbdev/tarp) command is a little utility for manipulating `tar` archives. Its `create` subcommand makes it particularly simple to construct tar archives from files. The `tarp create` command takes a recipe for building\n",
    "a tar archive that contains lines of the form:\n",
    "\n",
    "```\n",
    "archive-name-1 source-name-1\n",
    "archive-name-2 source-name-2\n",
    "...\n",
    "```\n",
    "\n",
    "The source name can either be a file, \"text:something\", or \"pipe:something\".\n",
    "\n",
    "## Programmatically in Python\n",
    "\n",
    "You can also create a WebDataset with library functions in this library:\n",
    "\n",
    "- `webdataset.TarWriter` takes dictionaries containing key value pairs and writes them to disk\n",
    "- `webdataset.ShardWriter` takes dictionaries containing key value pairs and writes them to disk as a series of shards\n",
    "\n",
    "Here is a quick way of converting an existing dataset into a WebDataset; this will store all tensors as Python pickles:\n",
    "\n",
    "```Python\n",
    "sink = wds.TarWriter(\"dest.tar\")\n",
    "dataset = open_my_dataset()\n",
    "for index, (input, output) in dataset:\n",
    "    sink.write({\n",
    "        \"__key__\": \"sample%06d\" % index,\n",
    "        \"input.pyd\": input,\n",
    "        \"output.pyd\": output,\n",
    "    })\n",
    "sink.close()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Storing data as Python pickles allows most common Python datatypes to be stored, it is lossless, and the format is fast to decode.\n",
    "However, it is uncompressed and cannot be read by non-Python programs. It's often better to choose other storage formats, e.g.,\n",
    "taking advantage of common image compression formats.\n",
    "\n",
    "If you know that the input is an image and the output is an integer class, you can also write something like this:\n",
    "\n",
    "```Python\n",
    "sink = wds.TarWriter(\"dest.tar\")\n",
    "dataset = open_my_dataset()\n",
    "for index, (input, output) in dataset:\n",
    "    assert input.ndim == 3 and input.shape[2] == 3\n",
    "    assert input.dtype = np.float32 and np.amin(input) >= 0 and np.amax(input) <= 1\n",
    "    assert type(output) == int\n",
    "    sink.write({\n",
    "        \"__key__\": \"sample%06d\" % index,\n",
    "        \"input.jpg\": input,\n",
    "        \"output.cls\": output,\n",
    "    })\n",
    "sink.close()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `assert` statements in that loop are not necessary, but they document and illustrate the expectations for this\n",
    "particular dataset. Generally, the \".jpg\" encoder can actually encode a wide variety of array types as images. The\n",
    "\".cls\" encoder always requires an integer for encoding.\n",
    "\n",
    "Here is how you can use `TarWriter` for writing a dataset without using an encoder:\n",
    "\n",
    "```Python\n",
    "sink = wds.TarWriter(\"dest.tar\", encoder=False)\n",
    "for basename in basenames:\n",
    "    with open(f\"{basename}.png\", \"rb\") as stream):\n",
    "        image = stream.read()\n",
    "    cls = lookup_cls(basename)\n",
    "    sample = {\n",
    "        \"__key__\": basename,\n",
    "        \"input.png\": image,\n",
    "        \"target.cls\": cls\n",
    "    }\n",
    "    sink.write(sample)\n",
    "sink.close()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since no encoder is used, if you want to be able to read this data with the default decoder, `image` must contain a byte string corresponding to a PNG image (as indicated by the \".png\" extension on its dictionary key), and `cls` must contain an integer encoded in ASCII (as indicated by the \".cls\" extension on its dictionary key).\n",
    "\n",
    "# Writing Filters and Offline Augmentation\n",
    "\n",
    "Webdataset can be used for filters and offline augmentation of datasets. Here is a complete example that pre-augments a shard and extracts class labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "from itertools import islice\n",
    "\n",
    "def extract_class(data):\n",
    "    # mock implementation\n",
    "    return 0\n",
    "\n",
    "def preproc(image):\n",
    "    image = transforms.ToTensor()(image)\n",
    "    # more preprocessing here\n",
    "    return image\n",
    "\n",
    "def augment_wds(input, output, maxcount=999999999):\n",
    "    src = wds.DataPipeline(\n",
    "        wds.SimpleShardList(input),\n",
    "        wds.tarfile_to_samples(),\n",
    "        wds.decode(\"pil\"),\n",
    "        wds.to_tuple(\"__key__\", \"jpg;png\", \"json\"),\n",
    "        wds.map_tuple(None, preproc, None),\n",
    "    )\n",
    "    with wds.TarWriter(output) as dst:\n",
    "        for key, image, data in islice(src, 0, maxcount):\n",
    "            print(key)\n",
    "            image = image.numpy().transpose(1, 2, 0)\n",
    "            image -= np.amin(image)\n",
    "            image /= np.amax(image)\n",
    "            sample = {\n",
    "                \"__key__\": key,\n",
    "                \"png\": image,\n",
    "                \"cls\": extract_class(data)\n",
    "            }\n",
    "            dst.write(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now run the augmentation pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e39871fd9fd74f55\n",
      "f18b91585c4d3f3e\n",
      "ede6e66b2fb59aab\n",
      "ed600d57fcee4f94\n",
      "ff47e649b23f446d\n"
     ]
    }
   ],
   "source": [
    "url = \"http://storage.googleapis.com/nvdata-openimages/openimages-train-000000.tar\"\n",
    "url = f\"pipe:curl -L -s {url} || true\"\n",
    "augment_wds(url, \"_temp.tar\", maxcount=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify that things worked correctly, let's look at the output file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e39871fd9fd74f55.cls\n",
      "e39871fd9fd74f55.png\n",
      "f18b91585c4d3f3e.cls\n",
      "f18b91585c4d3f3e.png\n",
      "ede6e66b2fb59aab.cls\n",
      "ede6e66b2fb59aab.png\n",
      "ed600d57fcee4f94.cls\n",
      "ed600d57fcee4f94.png\n",
      "ff47e649b23f446d.cls\n",
      "ff47e649b23f446d.png\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "tar tf _temp.tar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to preprocess the entire OpenImages dataset with a process like this, you can use your favorite job queueing or worflow system.\n",
    "\n",
    "For example, using Dask, you could process all 554 shards in parallel using code like this:\n",
    "\n",
    "```Python\n",
    "shards = braceexpand.braceexpand(\"{000000..000554}\")\n",
    "inputs = [f\"gs://bucket/openimages-{shard}.tar\" for shard in shards]\n",
    "outputs = [f\"gs://bucket2/openimages-augmented-{shard}.tar\" for shard in shards]\n",
    "results = [dask.delayed(augment_wds)(args) for args in zip(inputs, outputs)]\n",
    "dask.compute(*results)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the data is streaming from and to Google Cloud Storage buckets, so very little local storage is required on each worker.\n",
    "\n",
    "For very large scale processing, it's easiest to submit separate jobs to a Kubernetes cluster using the Kubernetes `Job` template, or using a workflow engine like Argo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whether you prefer `WebDataset` or `Dataset` is a matter of style."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Syntax for URL Sources\n",
    "\n",
    "The `SimpleShardList` and `ResampledShards` take either a string or a list of URLs as an argument. If it is given a string, the string is expanded using the `braceexpand` library. So, the following are equivalent:\n",
    "\n",
    "```Python\n",
    "ShardList(\"dataset-{000..001}.tar\")\n",
    "ShardList([\"dataset-000.tar\", \"dataset-001.tar\"])\n",
    "```\n",
    "\n",
    "The url strings in a shard list are handled by default by the `webdataset.url_opener` filter. It recognizes three simple kinds of strings: \"-\", \"/path/to/file\", and \"pipe:command\":\n",
    "\n",
    "- the string \"-\", referring to stdin\n",
    "- a UNIX path, opened as a regular file\n",
    "- a URL-like string with the schema \"pipe:\"; such URLs are opened with `subprocess.Popen`. For example:\n",
    "    - `pipe:curl -s -L http://server/file` accesses a file via HTTP\n",
    "    - `pipe:gsutil cat gs://bucket/file` accesses a file on GCS\n",
    "    - `pipe:az cp --container bucket --name file --file /dev/stdout` accesses a file on Azure\n",
    "    - `pipe:ssh host cat file` accesses a file via `ssh`\n",
    "\n",
    "It might seem at first glance to be \"more efficient\" to use built-in Python libraries for accessing object stores rather than subprocesses, but efficient object store access from Python really requires spawning a separate process anyway, so this approach to accessing object stores is not only convenient, it also is as efficient as we can make it in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Length Properties\n",
    "\n",
    "WebDataset instances are subclasses of `IterableDataset`. These instances are not supposed to have a `__len__` method, and some code actually tests for that.\n",
    "\n",
    "If you want to have a length property on your dataset, use the `with_length(n)` method with whatever length you would like to  set.\n",
    "\n",
    "If you want to change the size of the epoch, i.e., if you want to force the iterator to quit after a given number of samples or batches, use the `with_epoch` method.\n",
    "\n",
    "You can combine both methods; use `with_length` last."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tar Header Overhead\n",
    "\n",
    "Tar imposes a 512 byte overhead for each file stored in the archive. For most applications, this is not an issue because images and other content tends to be much larger.\n",
    "\n",
    "If you have datasets that contain large amounts of small files (e.g., text-only training, etc.), this overhead may become significant. In that case, you have several options:\n",
    "\n",
    "- store some or all of your sample in JSON, MsgPack, or CBOR format\n",
    "- gzip-compress your tar file (use .tgz instead of .tar); WebDatset will automatically decompress\n",
    "- pre-batch the data (not recommended)\n",
    "\n",
    "Both of the first options are very simple. To store your entire sample in MsgPack format, do something like this:\n",
    "\n",
    "```\n",
    "# Writing\n",
    "\n",
    "    ... construct sample ...\n",
    "    sample = dict(mp=sample)\n",
    "    writer.write(sample)\n",
    "\n",
    "# Reading\n",
    "\n",
    "    dataset = ... initial construction ...\n",
    "    dataset = dataset.map(sample: sample[\"mp\"])\n",
    "    ... use sample as usual ...\n",
    "```\n"
   ]
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   "source": [
    "# Related Libraries and Software\n",
    "\n",
    "The [AIStore](http://github.com/NVIDIA/aistore) server provides an efficient backend for WebDataset; it functions like a combination of web server, content distribution network, P2P network, and distributed file system. Together, AIStore and WebDataset can serve input data from rotational drives distributed across many servers at the speed of local SSDs to many GPUs, at a fraction of the cost. We can easily achieve hundreds of MBytes/s of I/O per GPU even in large, distributed training jobs.\n",
    "\n",
    "The [tarproc](http://github.com/tmbdev/tarproc) utilities provide command line manipulation and processing of webdatasets and other tar files, including splitting, concatenation, and `xargs`-like functionality.\n",
    "\n",
    "The [tensorcom](http://github.com/tmbdev/tensorcom/) library provides fast three-tiered I/O; it can be inserted between [AIStore](http://github.com/NVIDIA/aistore) and [WebDataset](http://github.com/tmbdev/webdataset) to permit distributed data augmentation and I/O. It is particularly useful when data augmentation requires more CPU than the GPU server has available.\n",
    "\n",
    "You can find the full PyTorch ImageNet sample code converted to WebDataset at [tmbdev/pytorch-imagenet-wds](http://github.com/tmbdev/pytorch-imagenet-wds)\n"
   ]
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